林其雄,陈畅,闫云凤,齐冬莲
LIN Qixiong,CHEN Chang,YAN Yunfeng,QI Donglian

• 1:广东电网有限责任公司广州供电局
• 2:浙江大学电气工程学院

摘要(Abstract)：

现有电力施工场景下关于工装穿戴的智能监管方案主要是针对安全帽，少有针对作业人员整体合规穿戴的相关方案。基于更细致的作业人员工装合规穿戴监管需求，提出一种二阶段的工装合规穿戴检测算法，包括人员定位阶段和人体区域工装合规检测阶段。针对现场人员工作姿态复杂的情况，结合特征金字塔网络和Guided Anchor提出一种基于Faster R-CNN优化的人员定位算法，在15 000余张现场采集样本构成的数据集上获得了91.1%的人员定位准确率，相比普通Faster R-CNN算法提升6.0%。二阶段检测算法在人体区域工装检测任务上获得92.9%的准确率，相比单阶段Faster R-CNN算法提升11.4%。
In the existing electric construction scenario,the intelligent supervision scheme for wearing workwear focuses on safety helmets,and there are few schemes for the overall workwear compliance of operators. In view of the more detailed requirements of workers′workwear compliance supervision,a two-stage compliance detection algorithm is proposed,which includes the personnel positioning stage and the human body area workwear compliance detection stage. Given the complex working posture of field personnel,a personnel positioning algorithm integrating FPN(feature pyramid network)and Guided Anchor based on Faster R-CNN is proposed. On the data set composed of more than 15, 000 on-site collected samples,a personnel positioning accuracy of 91.11% is obtained,6.0%higher than that of the ordinary Faster R-CNN scheme. The two-stage detection algorithm achieves an accuracy of92.9% in the human body area workwear detection task,which is 11.4% higher than the single-stage Faster R-CNN scheme.

关键词(KeyWords)： 合规穿戴;Guided Anchor;二阶段检测;人员定位;人体区域工装检测
wearing compliance;Guided Anchor;two-stage detection;personnel positioning;human body area workwear compliance detection

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 林其雄,陈畅,闫云凤,齐冬莲
LIN Qixiong,CHEN Chang,YAN Yunfeng,QI Donglian

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